We propose a new, highly effective and easy-to-implement algorithm for solving large-scale mean-variance optimization problems --- with weight upper bound constraints and without short sales --- when the size of mean-variance portfolios is much smaller than the number of assets, which is almost...

We consider second, third, fourth and fifth order stochastic dominance (SSD, TSD, FOSD and FISD, respectively) as well as decreasing absolute risk aversion (DARA) stochastic dominance (DSD). For comparison with DSD we also consider stochastic dominance (ESD) based on CARA utility functions....

Allocation between factor portfolios can bring significant advantages over traditional portfolio optimization performed among individual assets or asset classes. One such advantage is a substantial dimension reduction when one's attention turns from many assets to few factors. This, however,...

We define a regularized variant of the Dual Dynamic Programming algorithm called REDDP (REgularized Dual Dynamic Programming) to solve nonlinear dynamic programming equations. We extend the algorithm to solve nonlinear stochastic dynamic programming equations. The corresponding algorithm, called...

Following Levy and Roll [2010], we posit that the market portfolio is the efficient tangent Markowitz portfolio, i.e., it is mean-variance efficient. We then reverse engineer the expected returns and variance terms with constraints imposed by empirical data on a hierarchy of asset baskets. This...

We generalize the parametric portfolio policy framework to learning portfolio weights via deep neural networks. We find that network-based portfolio policies result in an increase of investor utility of between 30 and 100 percent over a comparable linear portfolio policy, depending on whether...

In this article, the authors propose an order flow simulator for meta orders such as those originating from the trading activity of buy-side firms. The simulator is designed with three key goals in mind. First, it should be simple to use and integrate into different applications. Second, it must...

When assets' expected returns follow a factor structure subject to pricing errors, we show that the mean-variance portfolio can be used to obtain a set of implied factor risk premia. Contrary to the instability of the mean-variance asset portfolio, we show that such implied factor risk premia...

While common machine learning algorithms focus on minimizing the mean-square errors of model fit, we show that genetic programming, GP, is well-suited to maximize an economic objective, the Sharpe ratio of the usual spread portfolio in the cross-section of expected stock returns. In contrast to...

This paper analyzes competition between mutual funds in a multiple funds version of the model of Hugonnier and Kaniel. We characterize the set of equilibria for this delegated portfolio management game and show that there exists a unique Pareto optimal equilibrium. The main result of this paper...